diff --git a/Dockerfile b/Dockerfile
new file mode 100644
index 0000000..18b1e4e
--- /dev/null
+++ b/Dockerfile
@@ -0,0 +1,20 @@
+FROM python:3.11-slim
+
+WORKDIR /app
+
+# Install system deps
+RUN apt-get update && apt-get install -y \
+ ffmpeg \
+ && rm -rf /var/lib/apt/lists/*
+
+# Install Python deps
+COPY backend/requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy code
+COPY backend/ ./backend/
+COPY frontend/ ./frontend/
+
+EXPOSE 8000
+
+CMD ["python", "backend/main.py"]
diff --git a/README.md b/README.md
index a356e81..bd7bd7b 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,27 @@
-# insightflow
+# InsightFlow
-音频与文档的领域知识构建平台 - 将会议录音转化为结构化知识图谱
\ No newline at end of file
+音频与文档的领域知识构建平台
+
+## 产品定位
+将会议录音和文档转化为结构化的知识图谱，通过人机回圈(Human-in-the-Loop)实现知识持续生长。
+
+## 核心特性
+- 🎙️ ASR 语音识别 + 热词注入
+- 🧠 LLM 实体抽取与解释
+- 🔗 双视图联动（文档视图 + 图谱视图）
+- 📈 知识生长（多文件实体对齐）
+
+## 技术栈
+- 前端: Next.js + Tailwind
+- 后端: Node.js / Python
+- 数据库: MySQL + Neo4j
+- ASR: Whisper
+- LLM: OpenAI / Kimi
+
+## 开发阶段
+- [ ] Phase 1: 骨架与单体分析 (MVP)
+- [ ] Phase 2: 交互与纠错工作台
+- [ ] Phase 3: 记忆与生长
+
+## 文档
+- [PRD v2.0](docs/PRD-v2.0.md)
diff --git a/backend/main.py b/backend/main.py
new file mode 100644
index 0000000..d013d40
--- /dev/null
+++ b/backend/main.py
@@ -0,0 +1,205 @@
+#!/usr/bin/env python3
+"""
+InsightFlow Backend - Phase 1 MVP with Deepgram
+ASR: Deepgram (Nova-3)
+Speaker Diarization: Deepgram
+LLM: Kimi API for entity extraction
+"""
+
+import os
+import json
+import httpx
+from fastapi import FastAPI, File, UploadFile, HTTPException, Form
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+from typing import List, Optional
+from datetime import datetime
+from deepgram import DeepgramClient, PrerecordedOptions, FileSource
+
+app = FastAPI(title="InsightFlow", version="0.1.0")
+
+app.add_middleware(
+ CORSMiddleware,
+ allow_origins=["*"],
+ allow_credentials=True,
+ allow_methods=["*"],
+ allow_headers=["*"],
+)
+
+# Models
+class Entity(BaseModel):
+ id: str
+ name: str
+ type: str
+ start: int
+ end: int
+ definition: Optional[str] = None
+
+class TranscriptSegment(BaseModel):
+ start: float
+ end: float
+ text: str
+ speaker: Optional[str] = "Speaker A"
+
+class AnalysisResult(BaseModel):
+ transcript_id: str
+ segments: List[TranscriptSegment]
+ entities: List[Entity]
+ full_text: str
+ created_at: str
+
+storage = {}
+
+# API Keys
+DEEPGRAM_API_KEY = os.getenv("DEEPGRAM_API_KEY", "")
+KIMI_API_KEY = os.getenv("KIMI_API_KEY", "")
+KIMI_BASE_URL = "https://api.kimi.com/coding"
+
+def transcribe_with_deepgram(audio_data: bytes, filename: str) -> dict:
+ """使用 Deepgram 进行转录和说话人分离"""
+ if not DEEPGRAM_API_KEY:
+ raise HTTPException(status_code=500, detail="DEEPGRAM_API_KEY not configured")
+
+ deepgram = DeepgramClient(DEEPGRAM_API_KEY)
+
+ payload: FileSource = {
+ "buffer": audio_data,
+ "mimetype": "audio/wav" if filename.endswith(".wav") else "audio/mp3"
+ }
+
+ options = PrerecordedOptions(
+ model="nova-3",
+ language="zh",
+ smart_format=True,
+ diarize=True, # 说话人分离
+ utterances=True,
+ punctuate=True,
+ paragraphs=True
+ )
+
+ response = deepgram.listen.prerecorded.v("1").transcribe_file(payload, options)
+
+ # 解析结果
+ result = response.results
+ full_text = result.channels[0].alternatives[0].transcript if result.channels else ""
+
+ # 提取带说话人的片段
+ segments = []
+ if result.utterances:
+ for u in result.utterances:
+ segments.append({
+ "start": u.start,
+ "end": u.end,
+ "text": u.transcript,
+ "speaker": f"Speaker {u.speaker}"
+ })
+
+ return {
+ "full_text": full_text,
+ "segments": segments
+ }
+
+def extract_entities_with_llm(text: str) -> List[Entity]:
+ """使用 Kimi API 提取实体"""
+ if not KIMI_API_KEY or not text:
+ return []
+
+ prompt = f"""请从以下会议文本中提取关键实体（专有名词、项目名、技术术语、人名等），并以 JSON 格式返回：
+
+文本：{text[:3000]} # 限制长度避免超限
+
+要求：
+1. 每个实体包含：name(名称), type(类型: PROJECT/TECH/PERSON/ORG/OTHER), start(起始字符位置), end(结束字符位置), definition(一句话定义)
+2. 只返回 JSON 数组，不要其他内容
+3. 确保 start/end 是字符在文本中的位置
+
+示例输出：
+[
+ {{"name": "Project Alpha", "type": "PROJECT", "start": 23, "end": 35, "definition": "Q3季度的核心项目"}},
+ {{"name": "K8s", "type": "TECH", "start": 37, "end": 40, "definition": "Kubernetes的缩写"}}
+]
+"""
+
+ try:
+ response = httpx.post(
+ f"{KIMI_BASE_URL}/v1/chat/completions",
+ headers={"Authorization": f"Bearer {KIMI_API_KEY}", "Content-Type": "application/json"},
+ json={
+ "model": "k2p5",
+ "messages": [{"role": "user", "content": prompt}],
+ "temperature": 0.1
+ },
+ timeout=60.0
+ )
+ response.raise_for_status()
+ result = response.json()
+ content = result["choices"][0]["message"]["content"]
+
+ # 解析 JSON
+ import re
+ json_match = re.search(r'\[.*?\]', content, re.DOTALL)
+ if json_match:
+ entities_data = json.loads(json_match.group())
+ entities = []
+ for i, e in enumerate(entities_data):
+ entities.append(Entity(
+ id=f"ent_{i+1}",
+ name=e["name"],
+ type=e.get("type", "OTHER"),
+ start=e["start"],
+ end=e["end"],
+ definition=e.get("definition", "")
+ ))
+ return entities
+ except Exception as e:
+ print(f"LLM extraction failed: {e}")
+
+ return []
+
+@app.post("/api/v1/upload", response_model=AnalysisResult)
+async def upload_audio(file: UploadFile = File(...)):
+ """上传音频并分析"""
+ content = await file.read()
+
+ # Deepgram 转录
+ print(f"Transcribing with Deepgram: {file.filename}")
+ dg_result = transcribe_with_deepgram(content, file.filename)
+
+ # 构建片段
+ segments = [
+ TranscriptSegment(**seg) for seg in dg_result["segments"]
+ ] or [TranscriptSegment(start=0, end=0, text=dg_result["full_text"], speaker="Speaker A")]
+
+ # LLM 实体提取
+ print("Extracting entities with LLM...")
+ entities = extract_entities_with_llm(dg_result["full_text"])
+
+ analysis = AnalysisResult(
+ transcript_id=os.urandom(8).hex(),
+ segments=segments,
+ entities=entities,
+ full_text=dg_result["full_text"],
+ created_at=datetime.now().isoformat()
+ )
+
+ storage[analysis.transcript_id] = analysis
+ print(f"Analysis complete: {analysis.transcript_id}, {len(entities)} entities found")
+ return analysis
+
+@app.get("/api/v1/transcripts/{transcript_id}", response_model=AnalysisResult)
+async def get_transcript(transcript_id: str):
+ if transcript_id not in storage:
+ raise HTTPException(status_code=404, detail="Transcript not found")
+ return storage[transcript_id]
+
+@app.get("/api/v1/transcripts")
+async def list_transcripts():
+ return list(storage.values())
+
+# Serve frontend
+app.mount("/", StaticFiles(directory="frontend", html=True), name="frontend")
+
+if __name__ == "__main__":
+ import uvicorn
+ uvicorn.run(app, host="0.0.0.0", port=8000)
diff --git a/backend/requirements.txt b/backend/requirements.txt
new file mode 100644
index 0000000..96004bb
--- /dev/null
+++ b/backend/requirements.txt
@@ -0,0 +1,7 @@
+fastapi==0.115.0
+uvicorn[standard]==0.32.0
+python-multipart==0.0.17
+deepgram-sdk==3.7.0
+httpx==0.27.2
+pydantic==2.9.2
+python-dotenv==1.0.1
diff --git a/docker-compose.yml b/docker-compose.yml
new file mode 100644
index 0000000..1a280d8
--- /dev/null
+++ b/docker-compose.yml
@@ -0,0 +1,13 @@
+version: '3.8'
+
+services:
+ insightflow:
+ build: .
+ ports:
+ - "18000:8000"
+ environment:
+ - DEEPGRAM_API_KEY=${DEEPGRAM_API_KEY}
+ - KIMI_API_KEY=${KIMI_API_KEY}
+ volumes:
+ - ./data:/app/data
+ restart: unless-stopped
diff --git a/frontend/EOF b/frontend/EOF
new file mode 100644
index 0000000..e69de29
diff --git a/frontend/app.js b/frontend/app.js
new file mode 100644
index 0000000..f187b72
--- /dev/null
+++ b/frontend/app.js
@@ -0,0 +1,10 @@
+const API_BASE = '/api/v1';
+async function upload(file) {
+ const formData = new FormData();
+ formData.append('file', file);
+ const res = await fetch(API_BASE + '/upload', {
+ method: 'POST',
+ body: formData
+ });
+ return await res.json();
+}
diff --git a/frontend/index.html b/frontend/index.html
new file mode 100644
index 0000000..f6570ee
--- /dev/null
+++ b/frontend/index.html
@@ -0,0 +1,18 @@
+
+
+
+
+ InsightFlow MVP
+
+
+
+ InsightFlow
+ Phase 1 MVP - 音频转录与实体提取
+ 拖拽音频文件上传
+
+